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Skillscope

AMD Agent Skills Python Claude Code GitHub Actions License: MIT

Skillscope

A test harness for AI agent skills. Install the harness, or point a workflow at your repo. Skillscope enables three main types of tests:

  • Structural: Checks whether the skill is well structured (contains the expected files, a proper hierarchy, etc.).
  • Routing: Checks whether the skill triggers when it should and stays quiet when it shouldn't. You may also point to more than one skill here to explore how skills interfere with each other's routing.
  • Behavioral: Runs the agent end to end and uses an LLM judge to confirm the skill behaves correctly.

All three read one dataset per skill, <skill>/evals/evals.json. How to write one is in docs/authoring-evals.md.

Important

Early Days: Flags, workflow inputs, and report formats may evolve quickly as this repo takes shape.

Quick start

Run from the root of the repo you want tested.

uv tool install --system-certs git+https://github.com/amd/skillscope

skillscope structural                        # no agent, no tokens
skillscope structural --external             # the same, plus checking external URLs
skillscope behavioral --skill my-skill       # needs an authenticated `claude` CLI
skillscope routing --routing-room my-skill,its-neighbour  # also needs a key in the environment

routing needs ANTHROPIC_API_KEY or ANTHROPIC_AUTH_TOKEN set, and refuses to start without one — a claude login on its own is not enough. The key is what lets the run point the CLI at its own config directory; without that, the runner's own ~/.claude/skills join the room for every case and the scores describe a room nobody asked for. behavioral installs one skill and grades what the agent produced, so a stray skill is at worst noise there, and a plain claude login is fine.

Run it from your CI

Simply add an evals.json file to your skill and a workflow that points to our reusable workflow. This will triger all graders to run directly from your repo.

Skillscope

jobs:
  evals:
    uses: amd/skillscope/.github/workflows/reusable.yml@v0.1.3
    secrets:
      api_key: ${{ secrets.ANTHROPIC_API_KEY }}
    with:
      skills: skills/*

You may also choose to customize or even disable some tests if you prefer it. See docs/usage.md for details.

Commands

Command Does
skillscope structural Skill folders, datasets, and markdown references.
skillscope routing Which skill fires, with several installed together.
skillscope behavioral What a skill does once it has fired.
skillscope select The CI plan for a change, as JSON.
skillscope list-skills The skills that have a dataset, as JSON.
skillscope template The dataset template a new skill starts from.

--help on any of them is the reference. Reports go to stdout as markdown, to $GITHUB_STEP_SUMMARY under Actions, and to JSON under .skillscope/runs/ in the repo under test — worth adding to .gitignore.

Docs

Development

python -m pip install -e .
python -m unittest discover -s tests -t .

Runtime dependencies are PyYAML for frontmatter, inspect-ai for the eval framework both engines are built on, and anthropic for the model client. inspect-ai became required when the last stdlib-only engine was removed; the test suite still runs on the standard library alone, and builds a throwaway repo in a temp directory rather than reading the tree it runs in.

License

MIT. See LICENSE.